Overview

Dataset statistics

Number of variables13
Number of observations5329730
Missing cells0
Missing cells (%)0.0%
Duplicate rows14754
Duplicate rows (%)0.3%
Total size in memory528.6 MiB
Average record size in memory104.0 B

Variable types

Categorical5
Numeric7
Unsupported1

Alerts

Dataset has 14754 (0.3%) duplicate rowsDuplicates
Timestamp has a high cardinality: 10368 distinct valuesHigh cardinality
CPU usage [%] has a high cardinality: 9298 distinct valuesHigh cardinality
Memory usage [%] has a high cardinality: 2171 distinct valuesHigh cardinality
CPU usage [MHZ] is highly overall correlated with Memory usage [KB] and 3 other fieldsHigh correlation
Memory usage [KB] is highly overall correlated with CPU usage [MHZ] and 3 other fieldsHigh correlation
Disk write throughput [KB/s] is highly overall correlated with CPU usage [MHZ] and 3 other fieldsHigh correlation
Network received throughput [KB/s] is highly overall correlated with CPU usage [MHZ] and 3 other fieldsHigh correlation
Network transmitted throughput [KB/s] is highly overall correlated with CPU usage [MHZ] and 3 other fieldsHigh correlation
CPU capacity provisioned [MHZ] is highly imbalanced (98.0%)Imbalance
Disk read throughput [KB/s] is highly skewed (γ1 = 21.61500983)Skewed
Disk write throughput [KB/s] is highly skewed (γ1 = 22.88582155)Skewed
Network received throughput [KB/s] is highly skewed (γ1 = 46.98962554)Skewed
Network transmitted throughput [KB/s] is highly skewed (γ1 = 31.31692524)Skewed
Timestamp is uniformly distributedUniform
Disk size [GB] is an unsupported type, check if it needs cleaning or further analysisUnsupported
Memory usage [KB] has 327072 (6.1%) zerosZeros
Disk read throughput [KB/s] has 4500772 (84.4%) zerosZeros
Disk write throughput [KB/s] has 1068770 (20.1%) zerosZeros
Network received throughput [KB/s] has 2410935 (45.2%) zerosZeros
Network transmitted throughput [KB/s] has 2443916 (45.9%) zerosZeros

Reproduction

Analysis started2023-09-30 09:41:14.645203
Analysis finished2023-09-30 09:43:31.842187
Duration2 minutes and 17.2 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Timestamp
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct10368
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size40.7 MiB
08.02.2016 07:00:00
 
523
08.02.2016 08:15:00
 
523
08.02.2016 07:40:00
 
523
08.02.2016 07:45:00
 
523
08.02.2016 07:50:00
 
523
Other values (10363)
5327115 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters101264870
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row04.01.2016 00:00:00
2nd row04.01.2016 00:05:00
3rd row04.01.2016 00:10:00
4th row04.01.2016 00:15:00
5th row04.01.2016 00:20:00

Common Values

ValueCountFrequency (%)
08.02.2016 07:00:00 523
 
< 0.1%
08.02.2016 08:15:00 523
 
< 0.1%
08.02.2016 07:40:00 523
 
< 0.1%
08.02.2016 07:45:00 523
 
< 0.1%
08.02.2016 07:50:00 523
 
< 0.1%
08.02.2016 07:55:00 523
 
< 0.1%
08.02.2016 08:00:00 523
 
< 0.1%
08.02.2016 08:05:00 523
 
< 0.1%
08.02.2016 08:10:00 523
 
< 0.1%
08.02.2016 08:20:00 523
 
< 0.1%
Other values (10358) 5324500
99.9%

Length

2023-09-30T09:43:31.952125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
08.02.2016 150624
 
1.4%
05.02.2016 150342
 
1.4%
06.02.2016 150048
 
1.4%
01.02.2016 149761
 
1.4%
03.02.2016 149760
 
1.4%
07.02.2016 149472
 
1.4%
04.02.2016 149184
 
1.4%
30.01.2016 149184
 
1.4%
02.02.2016 148888
 
1.4%
28.01.2016 148611
 
1.4%
Other values (314) 9163586
86.0%

Most occurring characters

ValueCountFrequency (%)
0 30282011
29.9%
1 15304190
15.1%
2 10893569
 
10.8%
. 10659460
 
10.5%
: 10659460
 
10.5%
6 6367252
 
6.3%
5329730
 
5.3%
5 4589650
 
4.5%
3 2298373
 
2.3%
4 1920479
 
1.9%
Other values (3) 2960696
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74616220
73.7%
Other Punctuation 21318920
 
21.1%
Space Separator 5329730
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30282011
40.6%
1 15304190
20.5%
2 10893569
 
14.6%
6 6367252
 
8.5%
5 4589650
 
6.2%
3 2298373
 
3.1%
4 1920479
 
2.6%
8 1037388
 
1.4%
7 1036829
 
1.4%
9 886479
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 10659460
50.0%
: 10659460
50.0%
Space Separator
ValueCountFrequency (%)
5329730
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 101264870
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30282011
29.9%
1 15304190
15.1%
2 10893569
 
10.8%
. 10659460
 
10.5%
: 10659460
 
10.5%
6 6367252
 
6.3%
5329730
 
5.3%
5 4589650
 
4.5%
3 2298373
 
2.3%
4 1920479
 
1.9%
Other values (3) 2960696
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101264870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30282011
29.9%
1 15304190
15.1%
2 10893569
 
10.8%
. 10659460
 
10.5%
: 10659460
 
10.5%
6 6367252
 
6.3%
5329730
 
5.3%
5 4589650
 
4.5%
3 2298373
 
2.3%
4 1920479
 
1.9%
Other values (3) 2960696
 
2.9%

CPU cores
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size40.7 MiB
2
3292311 
4
965682 
1
670847 
8
 
267303
6
 
133587

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5329730
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row6
3rd row6
4th row6
5th row6

Common Values

ValueCountFrequency (%)
2 3292311
61.8%
4 965682
 
18.1%
1 670847
 
12.6%
8 267303
 
5.0%
6 133587
 
2.5%

Length

2023-09-30T09:43:32.124163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T09:43:32.309807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 3292311
61.8%
4 965682
 
18.1%
1 670847
 
12.6%
8 267303
 
5.0%
6 133587
 
2.5%

Most occurring characters

ValueCountFrequency (%)
2 3292311
61.8%
4 965682
 
18.1%
1 670847
 
12.6%
8 267303
 
5.0%
6 133587
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5329730
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3292311
61.8%
4 965682
 
18.1%
1 670847
 
12.6%
8 267303
 
5.0%
6 133587
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common 5329730
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3292311
61.8%
4 965682
 
18.1%
1 670847
 
12.6%
8 267303
 
5.0%
6 133587
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5329730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3292311
61.8%
4 965682
 
18.1%
1 670847
 
12.6%
8 267303
 
5.0%
6 133587
 
2.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size40.7 MiB
0
5319365 
2048
 
10365

Length

Max length4
Median length1
Mean length1.0058343
Min length1

Characters and Unicode

Total characters5360825
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5319365
99.8%
2048 10365
 
0.2%

Length

2023-09-30T09:43:32.503648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-30T09:43:32.719262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5319365
99.8%
2048 10365
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 5329730
99.4%
2 10365
 
0.2%
4 10365
 
0.2%
8 10365
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5360825
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5329730
99.4%
2 10365
 
0.2%
4 10365
 
0.2%
8 10365
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 5360825
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5329730
99.4%
2 10365
 
0.2%
4 10365
 
0.2%
8 10365
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5360825
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5329730
99.4%
2 10365
 
0.2%
4 10365
 
0.2%
8 10365
 
0.2%

CPU usage [MHZ]
Real number (ℝ)

Distinct7834
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean259.60797
Minimum1
Maximum15528
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.7 MiB
2023-09-30T09:43:32.960275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q124
median69
Q3184
95-th percentile1258
Maximum15528
Range15527
Interquartile range (IQR)160

Descriptive statistics

Standard deviation636.51647
Coefficient of variation (CV)2.4518372
Kurtosis47.005543
Mean259.60797
Median Absolute Deviation (MAD)52
Skewness5.9423504
Sum1.3836404 × 109
Variance405153.21
MonotonicityNot monotonic
2023-09-30T09:43:33.334460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 97579
 
1.8%
20 93277
 
1.8%
10 93208
 
1.7%
21 92300
 
1.7%
9 90987
 
1.7%
22 87370
 
1.6%
15 81220
 
1.5%
18 80608
 
1.5%
23 75156
 
1.4%
17 73884
 
1.4%
Other values (7824) 4464141
83.8%
ValueCountFrequency (%)
1 14
 
< 0.1%
2 1344
 
< 0.1%
3 2752
 
0.1%
4 2025
 
< 0.1%
5 5015
 
0.1%
6 16178
 
0.3%
7 44097
0.8%
8 54021
1.0%
9 90987
1.7%
10 93208
1.7%
ValueCountFrequency (%)
15528 1
< 0.1%
12056 1
< 0.1%
12020 1
< 0.1%
11910 1
< 0.1%
11407 1
< 0.1%
11226 1
< 0.1%
11130 1
< 0.1%
11054 1
< 0.1%
11043 1
< 0.1%
11030 1
< 0.1%

CPU usage [%]
Categorical

Distinct9298
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size40.7 MiB
0,23
 
55912
0,22
 
51117
0,21
 
50040
0,18
 
45015
0,41
 
44539
Other values (9293)
5083107 

Length

Max length5
Median length4
Mean length3.9939093
Min length1

Characters and Unicode

Total characters21286458
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1021 ?
Unique (%)< 0.1%

Sample

1st row11,9
2nd row15,93
3rd row15,38
4th row11,93
5th row12,41

Common Values

ValueCountFrequency (%)
0,23 55912
 
1.0%
0,22 51117
 
1.0%
0,21 50040
 
0.9%
0,18 45015
 
0.8%
0,41 44539
 
0.8%
0,38 43200
 
0.8%
0,17 41735
 
0.8%
0,39 41651
 
0.8%
0,42 39478
 
0.7%
0,4 39103
 
0.7%
Other values (9288) 4877940
91.5%

Length

2023-09-30T09:43:33.730051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,23 55912
 
1.0%
0,22 51117
 
1.0%
0,21 50040
 
0.9%
0,18 45015
 
0.8%
0,41 44539
 
0.8%
0,38 43200
 
0.8%
0,17 41735
 
0.8%
0,39 41651
 
0.8%
0,42 39478
 
0.7%
0,4 39103
 
0.7%
Other values (9288) 4877940
91.5%

Most occurring characters

ValueCountFrequency (%)
, 5290545
24.9%
0 2706001
12.7%
1 2213032
10.4%
2 2018540
 
9.5%
3 1699984
 
8.0%
4 1553706
 
7.3%
5 1302626
 
6.1%
6 1202416
 
5.6%
7 1168920
 
5.5%
8 1105237
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15995913
75.1%
Other Punctuation 5290545
 
24.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2706001
16.9%
1 2213032
13.8%
2 2018540
12.6%
3 1699984
10.6%
4 1553706
9.7%
5 1302626
8.1%
6 1202416
7.5%
7 1168920
7.3%
8 1105237
6.9%
9 1025451
 
6.4%
Other Punctuation
ValueCountFrequency (%)
, 5290545
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21286458
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 5290545
24.9%
0 2706001
12.7%
1 2213032
10.4%
2 2018540
 
9.5%
3 1699984
 
8.0%
4 1553706
 
7.3%
5 1302626
 
6.1%
6 1202416
 
5.6%
7 1168920
 
5.5%
8 1105237
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21286458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 5290545
24.9%
0 2706001
12.7%
1 2213032
10.4%
2 2018540
 
9.5%
3 1699984
 
8.0%
4 1553706
 
7.3%
5 1302626
 
6.1%
6 1202416
 
5.6%
7 1168920
 
5.5%
8 1105237
 
5.2%
Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8873539
Minimum1048576
Maximum1.3421773 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.7 MiB
2023-09-30T09:43:34.019180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1048576
5-th percentile2097152
Q14194304
median8388608
Q310485760
95-th percentile20971520
Maximum1.3421773 × 108
Range1.3316915 × 108
Interquartile range (IQR)6291456

Descriptive statistics

Standard deviation10095093
Coefficient of variation (CV)1.1376626
Kurtosis90.250661
Mean8873539
Median Absolute Deviation (MAD)4194304
Skewness7.7426173
Sum4.7293567 × 1013
Variance1.019109 × 1014
MonotonicityNot monotonic
2023-09-30T09:43:34.330664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
8388608 1433188
26.9%
4194304 1203174
22.6%
2097152 1056096
19.8%
16777216 674636
12.7%
12582912 270124
 
5.1%
6291456 170476
 
3.2%
25165824 155506
 
2.9%
20971520 112865
 
2.1%
10485760 62203
 
1.2%
33554432 46940
 
0.9%
Other values (6) 144522
 
2.7%
ValueCountFrequency (%)
1048576 31104
 
0.6%
2097152 1056096
19.8%
3145728 38850
 
0.7%
4194304 1203174
22.6%
5242880 10368
 
0.2%
6291456 170476
 
3.2%
8388608 1433188
26.9%
10485760 62203
 
1.2%
12582912 270124
 
5.1%
14680064 41170
 
0.8%
ValueCountFrequency (%)
134217728 20736
 
0.4%
50331648 2294
 
< 0.1%
33554432 46940
 
0.9%
25165824 155506
 
2.9%
20971520 112865
 
2.1%
16777216 674636
12.7%
14680064 41170
 
0.8%
12582912 270124
 
5.1%
10485760 62203
 
1.2%
8388608 1433188
26.9%

Memory usage [KB]
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12980
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean705878.95
Minimum0
Maximum23652518
Zeros327072
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size40.7 MiB
2023-09-30T09:43:34.694745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q180531
median223137
Q3871996
95-th percentile3050098
Maximum23652518
Range23652518
Interquartile range (IQR)791465

Descriptive statistics

Standard deviation1135087.3
Coefficient of variation (CV)1.608048
Kurtosis15.391007
Mean705878.95
Median Absolute Deviation (MAD)191260
Skewness3.2564915
Sum3.7621442 × 1012
Variance1.2884231 × 1012
MonotonicityNot monotonic
2023-09-30T09:43:35.022799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 327072
 
6.1%
65431 25614
 
0.5%
41524 23971
 
0.4%
83467 22857
 
0.4%
55365 22773
 
0.4%
83047 22578
 
0.4%
75078 21852
 
0.4%
32716 21797
 
0.4%
66689 21157
 
0.4%
31877 21130
 
0.4%
Other values (12970) 4798929
90.0%
ValueCountFrequency (%)
0 327072
6.1%
1258 872
 
< 0.1%
2517 2532
 
< 0.1%
2726 1158
 
< 0.1%
3355 1
 
< 0.1%
3775 331
 
< 0.1%
3985 2746
 
0.1%
5033 4323
 
0.1%
5453 4589
 
0.1%
6291 483
 
< 0.1%
ValueCountFrequency (%)
23652518 1
< 0.1%
23014984 1
< 0.1%
21690424 1
< 0.1%
21489097 1
< 0.1%
21360751 1
< 0.1%
21036111 1
< 0.1%
20917834 1
< 0.1%
19928817 1
< 0.1%
19860868 1
< 0.1%
19526162 1
< 0.1%

Memory usage [%]
Categorical

Distinct2171
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size40.7 MiB
0
 
327072
0,99
 
79784
0,19
 
63864
0,39
 
60950
1,99
 
58218
Other values (2166)
4739842 

Length

Max length5
Median length4
Mean length4.0836266
Min length1

Characters and Unicode

Total characters21764627
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique289 ?
Unique (%)< 0.1%

Sample

1st row11,79
2nd row16,66
3rd row18,72
4th row17,99
5th row14,99

Common Values

ValueCountFrequency (%)
0 327072
 
6.1%
0,99 79784
 
1.5%
0,19 63864
 
1.2%
0,39 60950
 
1.1%
1,99 58218
 
1.1%
1,39 57123
 
1.1%
1,59 56736
 
1.1%
1,79 56421
 
1.1%
1,19 55149
 
1.0%
0,79 53404
 
1.0%
Other values (2161) 4461009
83.7%

Length

2023-09-30T09:43:35.424899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 327072
 
6.1%
0,99 79784
 
1.5%
0,19 63864
 
1.2%
0,39 60950
 
1.1%
1,99 58218
 
1.1%
1,39 57123
 
1.1%
1,59 56736
 
1.1%
1,79 56421
 
1.1%
1,19 55149
 
1.0%
0,79 53404
 
1.0%
Other values (2161) 4461009
83.7%

Most occurring characters

ValueCountFrequency (%)
, 5002547
23.0%
9 3411312
15.7%
1 2485106
11.4%
2 2449471
11.3%
5 2342441
10.8%
3 1537787
 
7.1%
0 1358543
 
6.2%
7 1054017
 
4.8%
4 833569
 
3.8%
6 754587
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16762080
77.0%
Other Punctuation 5002547
 
23.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 3411312
20.4%
1 2485106
14.8%
2 2449471
14.6%
5 2342441
14.0%
3 1537787
9.2%
0 1358543
 
8.1%
7 1054017
 
6.3%
4 833569
 
5.0%
6 754587
 
4.5%
8 535247
 
3.2%
Other Punctuation
ValueCountFrequency (%)
, 5002547
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21764627
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 5002547
23.0%
9 3411312
15.7%
1 2485106
11.4%
2 2449471
11.3%
5 2342441
10.8%
3 1537787
 
7.1%
0 1358543
 
6.2%
7 1054017
 
4.8%
4 833569
 
3.8%
6 754587
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21764627
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 5002547
23.0%
9 3411312
15.7%
1 2485106
11.4%
2 2449471
11.3%
5 2342441
10.8%
3 1537787
 
7.1%
0 1358543
 
6.2%
7 1054017
 
4.8%
4 833569
 
3.8%
6 754587
 
3.5%

Disk read throughput [KB/s]
Real number (ℝ)

SKEWED  ZEROS 

Distinct43857
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean535.22134
Minimum0
Maximum377917
Zeros4500772
Zeros (%)84.4%
Negative0
Negative (%)0.0%
Memory size40.7 MiB
2023-09-30T09:43:35.757825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile119
Maximum377917
Range377917
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6929.3295
Coefficient of variation (CV)12.946662
Kurtosis568.38459
Mean535.22134
Median Absolute Deviation (MAD)0
Skewness21.61501
Sum2.8525852 × 109
Variance48015607
MonotonicityNot monotonic
2023-09-30T09:43:36.107493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4500772
84.4%
1 102974
 
1.9%
2 50511
 
0.9%
3 36984
 
0.7%
4 28486
 
0.5%
5 28326
 
0.5%
6 27763
 
0.5%
7 20158
 
0.4%
8 16447
 
0.3%
9 13248
 
0.2%
Other values (43847) 504061
 
9.5%
ValueCountFrequency (%)
0 4500772
84.4%
1 102974
 
1.9%
2 50511
 
0.9%
3 36984
 
0.7%
4 28486
 
0.5%
5 28326
 
0.5%
6 27763
 
0.5%
7 20158
 
0.4%
8 16447
 
0.3%
9 13248
 
0.2%
ValueCountFrequency (%)
377917 1
< 0.1%
364553 1
< 0.1%
364343 1
< 0.1%
362952 1
< 0.1%
349304 1
< 0.1%
338886 1
< 0.1%
332627 1
< 0.1%
330769 1
< 0.1%
330530 1
< 0.1%
327355 1
< 0.1%

Disk write throughput [KB/s]
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct18158
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.00603
Minimum0
Maximum144988
Zeros1068770
Zeros (%)20.1%
Negative0
Negative (%)0.0%
Memory size40.7 MiB
2023-09-30T09:43:36.389728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q339
95-th percentile366
Maximum144988
Range144988
Interquartile range (IQR)38

Descriptive statistics

Standard deviation1533.4132
Coefficient of variation (CV)8.5662657
Kurtosis900.55949
Mean179.00603
Median Absolute Deviation (MAD)8
Skewness22.885822
Sum9.5405381 × 108
Variance2351356.1
MonotonicityNot monotonic
2023-09-30T09:43:36.596148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1068770
20.1%
1 447702
 
8.4%
2 292727
 
5.5%
3 248025
 
4.7%
4 188206
 
3.5%
5 151237
 
2.8%
6 131549
 
2.5%
7 130645
 
2.5%
8 107329
 
2.0%
12 84554
 
1.6%
Other values (18148) 2478986
46.5%
ValueCountFrequency (%)
0 1068770
20.1%
1 447702
8.4%
2 292727
 
5.5%
3 248025
 
4.7%
4 188206
 
3.5%
5 151237
 
2.8%
6 131549
 
2.5%
7 130645
 
2.5%
8 107329
 
2.0%
9 81262
 
1.5%
ValueCountFrequency (%)
144988 1
< 0.1%
140252 1
< 0.1%
126043 1
< 0.1%
121350 1
< 0.1%
120795 1
< 0.1%
120578 1
< 0.1%
120536 1
< 0.1%
120282 1
< 0.1%
120083 1
< 0.1%
120052 1
< 0.1%

Disk size [GB]
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size40.7 MiB

Network received throughput [KB/s]
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct13830
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.939552
Minimum0
Maximum112679
Zeros2410935
Zeros (%)45.2%
Negative0
Negative (%)0.0%
Memory size40.7 MiB
2023-09-30T09:43:36.801136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q315
95-th percentile365
Maximum112679
Range112679
Interquartile range (IQR)15

Descriptive statistics

Standard deviation912.16568
Coefficient of variation (CV)9.8146124
Kurtosis3628.734
Mean92.939552
Median Absolute Deviation (MAD)1
Skewness46.989626
Sum4.9534272 × 108
Variance832046.22
MonotonicityNot monotonic
2023-09-30T09:43:37.319094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2410935
45.2%
1 475790
 
8.9%
2 236873
 
4.4%
4 156336
 
2.9%
3 133878
 
2.5%
6 107180
 
2.0%
5 103768
 
1.9%
7 76689
 
1.4%
8 59802
 
1.1%
9 50854
 
1.0%
Other values (13820) 1517625
28.5%
ValueCountFrequency (%)
0 2410935
45.2%
1 475790
 
8.9%
2 236873
 
4.4%
3 133878
 
2.5%
4 156336
 
2.9%
5 103768
 
1.9%
6 107180
 
2.0%
7 76689
 
1.4%
8 59802
 
1.1%
9 50854
 
1.0%
ValueCountFrequency (%)
112679 1
< 0.1%
112440 1
< 0.1%
109753 1
< 0.1%
109585 1
< 0.1%
109462 1
< 0.1%
109067 1
< 0.1%
108992 1
< 0.1%
108774 1
< 0.1%
107470 1
< 0.1%
107268 1
< 0.1%

Network transmitted throughput [KB/s]
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct18722
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.61437
Minimum0
Maximum114219
Zeros2443916
Zeros (%)45.9%
Negative0
Negative (%)0.0%
Memory size40.7 MiB
2023-09-30T09:43:37.517575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q317
95-th percentile410
Maximum114219
Range114219
Interquartile range (IQR)17

Descriptive statistics

Standard deviation1434.8286
Coefficient of variation (CV)10.277084
Kurtosis1343.8706
Mean139.61437
Median Absolute Deviation (MAD)1
Skewness31.316925
Sum7.441069 × 108
Variance2058733.1
MonotonicityNot monotonic
2023-09-30T09:43:37.732651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2443916
45.9%
1 515513
 
9.7%
2 216341
 
4.1%
3 136453
 
2.6%
5 94958
 
1.8%
4 93756
 
1.8%
8 78981
 
1.5%
7 61576
 
1.2%
6 60415
 
1.1%
14 55991
 
1.1%
Other values (18712) 1571830
29.5%
ValueCountFrequency (%)
0 2443916
45.9%
1 515513
 
9.7%
2 216341
 
4.1%
3 136453
 
2.6%
4 93756
 
1.8%
5 94958
 
1.8%
6 60415
 
1.1%
7 61576
 
1.2%
8 78981
 
1.5%
9 42625
 
0.8%
ValueCountFrequency (%)
114219 1
< 0.1%
110097 1
< 0.1%
109700 1
< 0.1%
106189 1
< 0.1%
102215 1
< 0.1%
101110 1
< 0.1%
100981 1
< 0.1%
100794 1
< 0.1%
100353 1
< 0.1%
100348 1
< 0.1%

Interactions

2023-09-30T09:43:00.804564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:34.110602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:38.429078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:43.514864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:47.695616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:51.686344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:56.651079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:43:01.382662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:34.783580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:39.073348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:44.162134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:48.276594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:52.395125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:57.228574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:43:01.961903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:35.564272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:40.172628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:44.743624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:48.844096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:53.212505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:57.798360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:43:02.519374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:36.147484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:40.951951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:45.321461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:49.400176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:53.962564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:58.365719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:43:03.078796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:36.736531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:41.721343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:45.935130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:49.974971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:54.762949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:58.925437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:43:03.656344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:37.299606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:42.332368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:46.533275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:50.548394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:55.443563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:59.680336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:43:04.203747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:37.856518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:42.901956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:47.108527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:51.112197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:42:56.053760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-30T09:43:00.244013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-30T09:43:37.908285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
CPU usage [MHZ]Memory capacity provisioned [KB]Memory usage [KB]Disk read throughput [KB/s]Disk write throughput [KB/s]Network received throughput [KB/s]Network transmitted throughput [KB/s]CPU coresCPU capacity provisioned [MHZ]
CPU usage [MHZ]1.0000.4110.8190.4690.7550.6120.6050.1880.009
Memory capacity provisioned [KB]0.4111.0000.4110.2200.3440.1960.1990.3280.022
Memory usage [KB]0.8190.4111.0000.4880.7280.6410.6250.1680.013
Disk read throughput [KB/s]0.4690.2200.4881.0000.4890.3670.3830.0140.002
Disk write throughput [KB/s]0.7550.3440.7280.4891.0000.5350.5080.0160.002
Network received throughput [KB/s]0.6120.1960.6410.3670.5351.0000.9240.0140.000
Network transmitted throughput [KB/s]0.6050.1990.6250.3830.5080.9241.0000.0110.001
CPU cores0.1880.3280.1680.0140.0160.0140.0111.0000.035
CPU capacity provisioned [MHZ]0.0090.0220.0130.0020.0020.0000.0010.0351.000

Missing values

2023-09-30T09:43:07.691214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-30T09:43:14.822205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TimestampCPU coresCPU capacity provisioned [MHZ]CPU usage [MHZ]CPU usage [%]Memory capacity provisioned [KB]Memory usage [KB]Memory usage [%]Disk read throughput [KB/s]Disk write throughput [KB/s]Disk size [GB]Network received throughput [KB/s]Network transmitted throughput [KB/s]
004.01.2016 00:00:0060161911,920971520247254211,794983077099511255
104.01.2016 00:05:0060216715,9320971520349385516,6666033670913111701
204.01.2016 00:10:0060209315,3820971520392586818,72344883970912162626
304.01.2016 00:15:0060162211,9320971520377277617,995922937099511274
404.01.2016 00:20:0060168812,4120971520314363114,9967734970911411250
504.01.2016 00:25:0060156111,4820971520293391613,99244215709859972
604.01.2016 00:30:0060175012,8720971520247254211,7955629070910351378
704.01.2016 00:35:0060218216,0420971520335334615,99107581370914492479
804.01.2016 00:40:0060177613,0620971520289197313,7947429270913291530
904.01.2016 00:45:0060168712,420971520360500417,196202967099711307
TimestampCPU coresCPU capacity provisioned [MHZ]CPU usage [MHZ]CPU usage [%]Memory capacity provisioned [KB]Memory usage [KB]Memory usage [%]Disk read throughput [KB/s]Disk write throughput [KB/s]Disk size [GB]Network received throughput [KB/s]Network transmitted throughput [KB/s]
532972008.02.2016 23:10:0040590,83251658246845102,720550000
532972108.02.2016 23:15:0040370,52251658243498051,390150000
532972208.02.2016 23:20:0040370,52251658241988100,790150000
532972308.02.2016 23:25:0040360,51251658241484780,590150000
532972408.02.2016 23:30:0040360,51251658242491420,990150000
532972508.02.2016 23:35:0040370,51251658243321891,320150000
532972608.02.2016 23:40:0040360,5125165824981470,390050000
532972708.02.2016 23:45:0040360,525165824478150,190050000
532972808.02.2016 23:50:0040370,51251658241811940,720050000
532972908.02.2016 23:55:0040360,5125165824478150,190050000

Duplicate rows

Most frequently occurring

TimestampCPU coresCPU capacity provisioned [MHZ]CPU usage [MHZ]CPU usage [%]Memory capacity provisioned [KB]Memory usage [KB]Memory usage [%]Disk read throughput [KB/s]Disk write throughput [KB/s]Network received throughput [KB/s]Network transmitted throughput [KB/s]# duplicates
705313.01.2016 06:00:002090,2183886080000005
797915.01.2016 04:10:002090,283886080000005
21101.02.2016 11:45:002090,2183886080000004
34901.02.2016 19:15:002090,2183886080000004
57002.02.2016 05:45:002090,2183886080000004
106003.02.2016 05:55:002090,2183886080000004
179104.02.2016 01:45:002090,2183886080000004
205804.02.2016 15:00:002090,2183886080000004
258805.02.2016 00:10:002090,2183886080000004
261505.02.2016 01:30:002090,2183886080000004